Two Efficient Algorithms for Mining High Utility Sequential Patterns

Zhang, Chuankai; Zu, Yiwen; Nie, Junli; Du, Linzi

doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00132

Cited by 4 publications

(13 citation statements)

References 11 publications

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“…Although HUIM algorithms can extract interesting patterns in many real-life applications, they are not able to handle the sequence database where the timestamp is embedded in each item. Many high-utility sequential pattern mining algorithms have been proposed in the past decades [9,13,16,18,38], and high-utility sequential patterns can be extracted more efficiently with a series of novel data structures and pruning strategies proposed. Ahmed et al [13] first defined the problem of mining high-utility sequential patterns and proposed a novel framework for mining high-utility sequential patterns.…”

Section: High-utility Sequential Pattern Miningmentioning

confidence: 99%

“…Based on USpan, Alkan and Karagoz and Wang et al, respectively, proposed HuspExt [16] and HUS-Span [38] to increase efficiency of the mining process. Zhang et al [18] proposed an efficient algorithm named FHUSpan (named HUS-UT in the paper), which adopts a novel data structure named Utility-Table to store the sequence database in the memory and the TRSU strategy to reduce search space. Recently, Gan et al proposed two efficient algorithms named ProUM [40] and HUSP-ULL [41], respectively, to improve mining efficiency.…”

Section: High-utility Sequential Pattern Miningmentioning

confidence: 99%

“…In the first phase named Extension, we use the existing algorithm FHUSpan [18] which we proposed earlier to efficiently mine the general high-utility sequences (g-sequences). The main tasks of this phase are improving efficiency greatly of MUHU and extract g-sequences preparing for the next phase.…”

Section: Extension (Phase I): Find G-sequencesmentioning

confidence: 99%

“…Obviously, with this transformation, mining g-sequence is equivalent to mining high-utility sequences. So, we use the existing high-utility sequential pattern mining algorithm FHUSpan [18], which we proposed earlier to find g-sequences.…”

Section: Extension (Phase I): Find G-sequencesmentioning

confidence: 99%

“…The task of high-utility sequential pattern mining is to discover all high-utility sequential patterns (HUSPs, the sequences with high utility) from the quantitative sequence database with a predefined minimum utility threshold. Many high-utility sequential pattern mining algorithms have been proposed in the past decades [13,[16][17][18][19][20], and high-utility sequential patterns can be extracted more efficiently with a series of novel data structures and pruning strategies proposed. In addition, high-utility sequential pattern mining has many practical applications including web log data [21], mobile commerce environments [22], and gene regulation data.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

Zhang

2020

Wireless Communications and Mobile Computing

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High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences (g-sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation (s-sequences) as high-utility hierarchical sequential patterns from g-sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.

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Section: High-utility Sequential Pattern Miningmentioning

confidence: 99%

Section: High-utility Sequential Pattern Miningmentioning

confidence: 99%

Section: Extension (Phase I): Find G-sequencesmentioning

confidence: 99%

Section: Extension (Phase I): Find G-sequencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

Zhang

2020

Wireless Communications and Mobile Computing

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MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

Sujit R Wakchaure

2023

IJRITCC

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One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

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Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

Cao,

Duan

2023

Applied Sciences

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High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

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Two Efficient Algorithms for Mining High Utility Sequential Patterns

Cited by 4 publications

References 11 publications

An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

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